138 results about "Process quality" patented technology

The invention provides an MES-based power distribution cabinet quality inspection analysis and process regulation and control method and system, and the method comprises the steps: converting a quality inspection standard into an executable rule, generating a standardized quality inspection template, evaluating the quality of a single project and a batch, monitoring the mean value and fluctuation of a production process based on a statistical process control theory, and calculating a process capability index. A dynamic threshold value formula is combined to adjust a control limit in real time, process stability is evaluated, equipment data is standardized, production batches are associated, data consistency is verified and ensured, graded early warning is performed according to the deviation degree, a machine learning model is combined to realize predictive maintenance, process parameters are dynamically corrected, historical data are analyzed through machine learning, and the process quality is improved. According to the method, high-order feature quantification influence factors are extracted, abnormal parameter combination is detected in combination with an isolated forest, a process strategy library is updated, closed-loop optimization is formed, full-process automation from data acquisition, real-time analysis and parameter adaptive adjustment to predictive maintenance is realized, and quality inspection efficiency and process stability are improved.

The invention discloses a process parameter tracing and quality collaborative management system for ceramic production, and relates to the technical field of ceramic production, the management system comprises a step of obtaining a plurality of process data and quality data in a ceramic production process, and each group of process data comprises a raw material ratio, a forming pressure, a firing temperature and a firing time. According to the process parameter tracing and quality collaborative management system for ceramic production, a specific process parameter deviation link can be quickly positioned by reversely tracing to a raw material source from a finished product quality problem and combining batch association identifiers and data records of all links; the quality problem solving efficiency is improved, and batch loss caused by traceability lag is avoided; besides, a process quality association relationship constructed by the module is established, a quality detection result is closely associated with process parameters, and multi-dimensional analysis is performed, so that a quality problem can be fed back to process parameter adjustment in time, and targeted measures can be taken according to an analysis result.

The invention discloses a wire harness manufacturing management system and method based on artificial intelligence, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: inputting a structured feature vector and a process constraint condition into a multi-target reinforcement learning model, and generating a process parameter instruction; the process parameter instruction is executed, the product yield is monitored in real time, and cross-process quality tracing is carried out on the abnormal product yield; historical data of abnormal batches in crimping, assembling and testing procedures are traced, the defect root cause probability of each procedure is calculated based on a Bayesian causal network, and primary and secondary root causes of quality defects are obtained and analyzed; and dynamically updating process constraint conditions according to primary and secondary root cause analysis results, adjusting a reward function of the multi-target reinforcement learning model, and re-optimizing a process parameter instruction. According to the invention, the operation state of the production equipment is accurately captured, the production process is optimized and adjusted in real time, and a closed-loop management process is formed, so that the production flexibility, efficiency and product quality are remarkably improved.

The invention provides a numerical control grinding machine product quality real-time monitoring and optimizing method fused with the Internet of Things, and relates to the technical field of numerical control grinding machine control. Processing parameter retrieval is carried out; performing frequent item mining on the historical processing data; performing fluctuation centralized identification on the historical vibration parameter set according to the vibration parameter tolerance interval; performing deviation analysis on the historical process quality parameters by using the process quality parameter tolerance interval; performing process monitoring step length identification based on the vibration parameter fluctuation coefficient and the process quality deviation coefficient; and the monitoring branch is used for extracting and analyzing real-time monitoring data and optimally controlling the numerical control grinding machine. By means of the method and device, the technical problem that in the prior art, due to the fact that the adaptability of monitoring step length adjustment is insufficient, key parameters are difficult to adjust in time, and the product quality is affected can be solved, through deep analysis of historical data, refined management and optimization of the machining process of the numerical control grinding machine are achieved, and the product quality is improved.

The invention discloses a process quality control method combining FMEA analysis and a multivariate control chart. The method comprises the following steps: planning and preparing PFMEA; identifying special quality indexes of the process parameter diagram; a control limit and an abnormal mode are determined through failure analysis and risk analysis in the PFMEA, so that abnormal fluctuation in the product machining process is accurately identified; performing multivariate quality index decoupling and correlation analysis; constructing a multivariate SPC control chart; performing abnormal mode identification and source tracing on the multivariate SPC control chart; pFMEA optimization is combined with an SPC control chart to adjust the sampling frequency and the control limit. According to the method, the technical problems that the product quality is difficult to control through frequent detection during large part machining, a single-variable SPC control chart is inaccurate in abnormal mode recognition, the SPC control chart cannot be formulated according to the actual production process, and the production process cannot be effectively improved by the SPC technology are solved.

The invention discloses a manufacturing quality analysis system and method based on a large model Agent and MCP, and mainly relates to the field of intelligent manufacturing and artificial intelligence application. Comprising the following steps: S1, initializing a setting stage layer; s2, a data input stage layer; s3, a data processing stage layer; s4, an intelligent analysis stage; and S5, a result output stage. The system further comprises a large model agent Agent module. An agent Agent selection module; a data acquisition module; a data preprocessing module; and an analysis and report generation module. The method has the beneficial effects that through combined application between the large model Agent and the large model MCP, the process quality data processing analysis efficiency can be improved by more than 30%, and the quality analysis and decision suggestion output of the six-sigma black band level is provided through the AI; the limitations of an existing system in the aspects of strong manual dependence, lagging analysis timeliness, lack of quality talents and the like are solved.

The invention provides an optimization method and device for a blade casting process, equipment and a storage medium, and the method comprises the steps that process parameters of at least one process of blade casting are obtained; generating input sequence data based on the process parameters; inputting the input sequence data into a pre-trained process quality prediction model to obtain a predicted casting qualification rate and a forging defect occurrence probability output by the process quality prediction model; wherein the process quality prediction model is a transformation neural network model, and the process quality prediction model has learned in advance to obtain process parameters based on a blade casting process, and has the capability of predicting the qualification rate of castings and the occurrence probability of forging defects; and optimizing the process parameters based on the predicted qualified rate of the casting and the occurrence probability of the forging defects. According to the technical scheme, the forging qualification rate of the blade can be increased, and the development iteration frequency and period of the turbine blade casting process are reduced.

The invention relates to the technical field of comprehensive factory control, in particular to a mold whole-process quality monitoring system and device and a storage medium, and the system comprises a data collection and synchronization module which is connected with a plurality of mold manufacturing machine tool connection networks, collects machining data and operation states of mold manufacturing machine tools, generates a real-time data flow, and carries out the real-time processing of the mold manufacturing machine tools based on the real-time data flow. And analyzing the working efficiency and state of the mold manufacturing machine tool to obtain an equipment state result. According to the method, the machining data and the operation state of the mold manufacturing machine tool are collected in real time, the real-time data flow is formed, the machine tool operation efficiency and the equipment state are dynamically analyzed, state monitoring and production quality prediction in the mold manufacturing process are achieved, and the transparency and the management and control capacity of the mold machining link are improved; and according to the quality prediction result obtained through analysis, the production line performance and the equipment efficiency are dynamically evaluated, the production tasks are scheduled in real time, the equipment load distribution is optimized, a dynamic production scheduling system is formed, and the production efficiency and the resource utilization rate are improved.

The invention relates to the field of process optimization, in particular to a steelmaking process optimization method and system based on a digital twin model. A steelmaking process optimization system based on a digital twin model comprises a converter digital twin model construction module, a physical parameter adjustment module, a converter wear model construction module and a steelmaking process optimization module. By establishing the high-precision converter digital twinning model, heat transfer, flow field change and chemical reaction in the steelmaking process can be accurately described, a physical rule basis is provided for analogue simulation, and then component information of input materials and converter process parameters are optimized and adjusted by utilizing analogue simulation, so that the converter process parameters are optimized and adjusted, and the converter process parameters are optimized and adjusted. During the period, the flame image is used as a process quality evaluation index, so that the accuracy and efficiency of the steelmaking process are remarkably improved, and the dependence on experience of operators is reduced.

The invention discloses a quality inspection method and system for a new material production and processing process, and relates to the technical field of material quality inspection, and the method comprises the steps: collecting different batches of new material sample images after processing at each production stage; orderly classifying and marking the new material sample images according to batches and processes to serve as a training set; constructing and training a feature extraction model of new material features based on the training set, and obtaining feature vectors of the new materials in each production stage through the feature extraction model; constructing a twin neural network model, and obtaining the similarity between the feature vector of the new material in each production stage and the standard feature vector of the new material in each production stage through the twin neural network model; and judging the quality of the new material in each production stage based on a similarity result. A comprehensive new material production and processing process quality detection system is formed, quality problems can be effectively prevented and found for specific types of new materials, and consistency and reliability of product quality are guaranteed.

The invention relates to a manufacturing process quality tracing and root cause analysis system based on a data graph, and belongs to the technical field of manufacturing process quality control. The system performs cleaning, duplicate removal and standardization processing on multi-source heterogeneous data in the whole manufacturing process through a data processing module to generate structured data; the data graph construction module fuses the manufacturing domain knowledge ontology, constructs a manufacturing process digital twin semantic model and a multi-dimensional manufacturing data graph, introduces a dynamic association weight updating mechanism and mines node potential implicit association; the quality tracing module positions map nodes based on query semantic vectors, and reconstructs an entity state change track by using a digital twin semantic model to generate a tracing link; and the root cause analysis module screens candidate abnormal factors, locates a root cause in combination with dynamic weight and multi-scene simulation verification, and generates a quality optimization scheme. According to the invention, full-link accurate tracing and root cause efficient positioning of the manufacturing process quality are realized, and the intelligence and refinement level of manufacturing quality management and control is improved.

The invention discloses a flexible manufacturing electronic production line dynamic scheduling system and method, and relates to the technical field of intelligent manufacturing, and the system collects the operation state data of a production line in real time through sensor groups installed at all stations of the production line, and obtains a load fluctuation data set and an equipment operation data set after data processing; carrying out load state evaluation by calculating a dynamic load disturbance index Lfz, evaluating the load state of the production line, executing process adaptability analysis when the production line is evaluated to be in a standard operation load, calculating a production process adaptability index Bsc, and analyzing the influence of the load on the production process; comprehensive process evaluation is carried out through the comprehensive scheduling response index Sdd, and whether the process is qualified or not is evaluated; and intelligent scheduling and optimization are carried out on the production process according to scheduling instructions generated by two times of evaluation. The system can dynamically respond to the change of the production line, improve the production efficiency and flexibility, reduce resource waste and ensure the process quality.

The invention discloses a complex electronic equipment manufacturing process model simulation verification and optimization method. The method comprises the following steps: acquiring a process flow diagram in a manufacturing process model; performing chart conversion, resource library analysis and process flow diagram analysis on image-text data in the obtained process flow diagram, and taking data obtained by the chart conversion, the resource library analysis and the process flow diagram analysis as an input file of manufacturing process model simulation; carrying out operational analysis on the input file, and giving out warning and error information prompts; and modifying the manufacturing process model on the basis of a set rule and a production operation historical data optimization method until no error occurs in simulation. According to the method, the workload of repeatedly modifying the manufacturing process model can be reduced, the matching property of the manufacturing process model and the actual business is improved, and the predictability of the production process is improved, so that the optimization of the product process route, the controllable process quality and the achievable delivery plan are ensured.

The invention provides a multi-variety variable-batch manufacturing process quality characteristic evaluation method and system. The method comprises the steps that the total number of processes, the total number of products and the total number of poor processes in product historical production task process quality data are clustered according to product similarity and then combined; the total process number N, the total number N (X) of all the processes, the work report amount Nb of the type b products, the X process defect number P (X) and the total process defect number P are calculated; dimension factors of quality evaluation are calculated, the dimension factors comprise the procedure support degree SX, the procedure confidence coefficient CX, the procedure bad support degree SPX and the procedure reject ratio PRX, the quality characteristic PQX of each procedure is calculated accordingly, and the procedure with the large relative value is recognized as the current quality bottleneck procedure. According to the method, standardized, self-adaptive and convenient quality characteristic comprehensive evaluation can be carried out on discrete manufacturing type product procedures under the conditions of mixed lines of different product types, batch production, different procedure complexity and long technological process by integrating procedure historical production quality abnormity records.

The invention relates to a multi-modal data fusion 3C assembly process quality accurate prediction method, and belongs to the field of 3C intelligent manufacturing, and the method comprises the steps: carrying out the multi-modal data fusion, and forming a unified feature space containing process parameters and quality characterization; performing multi-dimensional analysis on process quality, performing hierarchical analysis on structured process parameters and unstructured images, and establishing cross-modal feature mapping; predicting the process quality, evaluating the contribution degree of process parameters to the quality by utilizing an integrated learning model based on a fused feature set, screening key parameters to reduce the complexity of the model, dynamically adjusting the prediction model, designing a'sliding window + multi-dimensional index 'monitoring system, and evaluating the model performance and the data distribution drift in real time. Establishing a time-performance-distribution three-dimensional trigger mechanism, and starting an adjustment strategy according to different priorities; a'cleaning-complementation-mechanism-mapping-self-adaption 'closed loop is realized, and the timeliness and accuracy of 3C assembly quality prediction are remarkably improved.

The invention relates to the technical field of rubber corrugated pipe processing quality detection. The invention relates to an effect evaluation method for processing quality detection of a rubber corrugated pipe. The method comprises the following steps: S1, acquiring process parameters of rubber corrugated pipe production equipment; s2, acquiring historical production data, performing finished product data screening on the historical production data in combination with the process parameters, and establishing a finished product database; s3, collecting a finished product image of the rubber corrugated pipe, and performing similarity matching analysis on the finished product image and the finished product database; according to the method, analysis and decision making are carried out based on a large amount of production data, errors caused by subjective judgment are avoided, in the abnormal scoring link, weights are distributed according to the importance degree of difference characteristics, the quality problem is quantified, data support and a reasonable detection strategy are provided for quality detection and improvement, high-risk products are detected preferentially, and the detection efficiency is improved. Unnecessary detection cost is reduced, and reworking and scrapping cost caused by unqualified products is reduced at the same time.

The invention discloses a complex process industry-oriented multi-level progressive parameter optimization method, which comprises the following steps of: analyzing the correlation between M process parameters and a quality index one by one to determine the importance degree of each process parameter to the quality index; sorting the importance degrees of the quality indexes in a descending order according to the process parameters to obtain the process parameters sorted according to the importance degrees; according to the sorted technological parameters, a technological parameter layering method based on correlation analysis is constructed, hierarchical division of the technological parameters is achieved, and a multi-level technological parameter combination is obtained through C times of division; modeling optimization is carried out in sequence from the Cth layer of technological parameter combination to the first layer of technological parameter combination, and the optimal solution of the first layer of technological parameter combination to the optimal solution of the Cth layer of technological parameter combination are obtained. According to the method, the problem of optimization difficulty caused by low modeling prediction precision and various process parameters in complex process manufacturing is effectively solved, and the method is of great significance in guaranteeing the process quality and improving the processing efficiency for process manufacturing enterprises.

The invention relates to a toilet paper roll core forming intelligent control method and system based on data driving, and the method comprises the following steps: S1, obtaining production process parameters and equipment operation state data, and carrying out the preprocessing; s2, constructing a mapping model of process quality and parameter data of production based on SVR; s3, on the basis of the mapping model, performing quality prediction on the technological process, and on the basis of a multi-objective optimization algorithm, obtaining optimization control parameters; s4, pushing the optimized control parameters to an intelligent control system, and realizing closed-loop control by using a fuzzy PID control algorithm; and S5, dynamically feeding back the actual output process data collected in real time to the modeling link to form a complete closed-loop feedback mechanism. According to the method, the environment change and the process disturbance can be dynamically responded in the production process, so that the purposes of improving the product quality, improving the production efficiency and reducing the resource consumption are achieved.

The invention discloses a manufacturing process quality diagnosis method based on an adaptive multivariate control chart. The method comprises the following steps: step 1, constructing an adaptive multivariate exponential weighted moving average control chart; and step 2, constructing a fuzzy support vector machine diagnosis model of firefly algorithm collaborative optimization, and using the model to complete manufacturing process quality diagnosis. According to the method, normal fluctuation and abnormal fluctuation areas in the manufacturing process can be identified, and accurate tracing of abnormal modes is realized, so that quality diagnosis of the manufacturing process is completed.

The invention provides a data analysis method and system, and the method comprises the steps: obtaining finished product quality inspection data and a production log in a preset time period, dividing qualified products into indirect qualified products and direct qualified products according to the finished product quality inspection data and the production log, and calculating a first-pass yield and a quality hidden danger score; extracting a key parameter set by adopting a mode of combining a dynamic sliding window and unsupervised feature selection based on the whole-flow process parameters of the preset time period; performing parameter abnormality judgment on the key parameter set of the indirect qualified product and the waste product, and mining an abnormal parameter combination by adopting a mode mining algorithm to obtain an operation problem item; and constructing a cause analysis table according to the first-pass yield, the classification information, the potential quality hazard score and the operation problem item of the finished product. According to the method and the device, the screening efficiency and accuracy of the key parameters are improved, and fine identification of potential quality hazards, accurate identification of operation problem items and visual display of process quality analysis are realized.

The invention discloses a large process model optimization method and system based on multi-process matching architecture driving, and the method comprises the steps: 1, obtaining the process parameters of each process in a multi-process process production line, and constructing a process parameter data set; 2, constructing a large process quality prediction model according to the operation mechanism and relevance of each process in the multi-process process production line; 3, training and testing the process quality prediction large model by adopting the process parameter data set to obtain a pre-trained process quality prediction large model; 4, inputting the process parameter combination of each current process in the multi-process process production line into the pre-trained process quality prediction large model to obtain a current process quality prediction result; judging whether the current process quality prediction result reaches the standard or not; if the technological parameters reach the standard, the technological parameter combination of the current technologies is the optimal technological parameter combination, and the multi-procedure technological production line is optimized based on the optimal technological parameter combination; and if not, adjusting the process parameter combination, and executing the step 4 on the basis of the adjusted process parameter combination.

The invention provides a manufacturing process quality improvement method based on multi-source data perception in order to solve the problem that an existing method can only achieve quality prediction and cannot achieve quality improvement. The method comprises the following steps: sensing multi-source heterogeneous data during manufacturing, and extracting knowledge from the multi-source heterogeneous data to construct a manufacturing process technology knowledge graph; a Resnet-LSTM quality prediction model is constructed and trained to predict defect types, key factors influencing defects are determined in combination with a manufacturing process technology knowledge graph, a multi-source sensor is deployed based on the key factors, multi-source sensor data influencing the defects can be sensed more accurately, an attention mechanism is introduced to perform weighted summation on the multi-source sensor data, and the defect detection accuracy is improved. The method comprises the following steps of: inputting the data into a trained CNN-ABILSTM quality improvement model to obtain a more accurate and credible quality prediction result, generating a process adjustment scheme capable of improving the manufacturing quality according to the quality prediction result in combination with a manufacturing process process knowledge graph, and improving the quality control and problem traceability during the manufacturing period.

The invention discloses a product assembly abnormal process parameter tracing method and device, and relates to the technical field of abnormal parameter tracing, the method takes a knowledge graph as a core technical means, and adopts a knowledge-driven ammunition assembly process quality control model to guide ammunition assembly process quality control. According to the method, the Bayesian network model and the knowledge traceability strategy are fused to help deeply identify and understand the root cause of the assembly quality problem, and powerful support is provided for systematic solution of the problem. The method can comprehensively evaluate the influence capability of process technological parameters on quality parameters, intelligently evaluate the quality defect category and predict the assembly quality consistency, and realizes the quality control of the ammunition assembly process.

The invention relates to a fabricated cavity component intelligent production system based on an SPCS system and a whole-process quality control method. The system comprises a modular flexible production line, an intelligent turnover machine and a concrete pouring intelligent cooperative control system, and efficient and intelligent production of components is achieved. According to the full-process quality control method, high quality and stability of production are ensured through multi-dimensional quality defect AI pre-judgment, process parameter dynamic optimization, full-life-cycle quality tracing and a self-adaptive process correction mechanism. The production efficiency is improved, the labor dependence is reduced, the environmental pollution is reduced, and a design-production-construction-operation and maintenance whole industrial chain solution is provided for the prefabricated building industry.

This invention relates to an intermediate compound of MK-8876 and its preparation method, providing a highly efficient synthetic method for preparing MK-8876. It boasts advantages such as high efficiency, low cost, stable and safe process, controllable process quality, and the potential for industrial-scale production, thus holding significant potential in this field.

The present invention discloses a method for analyzing monitoring data during a laser selective melting forming process, comprising: S1: stability analysis of the X-axis and Y-axis within the forming area; S2: stability analysis of the Z-axis within the forming area; S3: sensitivity analysis: calculating the sensitivity of grayscale fluctuations caused by changes in process parameters; and S4: system threshold calibration value. The present invention can monitor the forming state of a product during the laser selective melting forming process, establish a comparative relationship between monitoring data and forming quality, and can be applied to the production of metal additive manufacturing products. It can quickly and efficiently complete the analysis of large amounts of data, thereby ensuring the forming quality of the product and achieving quality traceability, improving full-process quality control, and resolving the problem of poor repeatability and quality stability of the laser selective melting forming process limiting its mass production, thereby promoting the application of the laser selective melting forming process in the mass production of more difficult and high-demand parts.

The utility model provides a combined frog point rail. The combined frog point rail is composed of a point rail A and point rail spacer iron B which are integrally formed. And the integrally formed combined frog point rail is a combined frog point rail manufactured by adopting an I-shaped point rail blank C with a uniform section. The technical problems that an existing point rail is large in manufacturing range, low in material utilization rate, high in manufacturing cost, poor in process quality stability, low in operability and inconvenient to popularize in the market are solved. The utility model discloses can satisfy factory batch production requirement, the manufacturing cost is relatively low, has higher popularization value.

The invention provides a window manufacturing apparatus and a window manufacturing method using the same. The window manufacturing apparatus may include: a melting unit configured to supply molten glass; and a shape processing unit disposed adjacent to the melting unit in a direction in which the molten glass is discharged from the melting unit. The shape processing unit may include a protruding portion that forms a groove in the preliminary glass discharged from the melting unit, so that glass manufacturing and shape processing can be continuously performed, thereby improving process quality of the manufactured window and improving process economy.

The application discloses an industrial full-link through implementation method based on unique identification and double contract anchoring, and belongs to the technical field of intelligent manufacturing informatization. The method takes a plan number as a unique and unchangeable identification throughout the full life cycle from raw material warehousing to finished product warehousing, takes a contract hash value as a cross-table association anchor point, adopts a step-by-step anchoring mechanism of sales contract priority binding and procurement contract subsequent binding, and combines batch processing, transaction safety control and full-chain backtracking synchronization technology to realize bidirectional synchronous updating of procurement and sales contract information in full life cycle data tables such as raw material inventory tables and production process quality inspection record tables. The application breaks the strong coupling of the procurement and production processes, supports cost information post-recording, solves the pain points of data disconnection, untraceability, low efficiency and maintenance difficulty of traditional industrial software, has a simple architecture, high performance and stable operation, and is suitable for various intelligent manufacturing systems such as MES and ERP. The application has been applied in actual industrial scenes and has industrialization popularization capability.

The invention discloses a PCB manufacturing process quality prediction and control method based on multi-modal data. The method comprises the following steps: collecting the multi-modal data in a key process for preprocessing; constructing a process semantic chain and carrying out feature coding to obtain a process semantic chain feature sequence; cross-modal attention fusion is carried out in the processes, coupling between the processes is realized through bidirectional cross-process attention and a process residual path, and process contribution degree decomposition is carried out to obtain cross-process joint features; executing quality risk prediction under a shared coding structure through a defect risk probability prediction model; and a technological parameter adjustment scheme is generated and issued for execution, execution feedback data and a quality detection result are collected, and online feedback and model self-correction are completed. The multi-mode cross-process fusion and defect risk probability prediction method is utilized to realize prospective evaluation and adaptive process control of the PCB process quality, and the method has the advantages of accurate prediction, sufficient explanation and intelligent regulation and control.